Customers, administrators, and other users often utilize data logs to analyze their resources and identify any trends or anomalies that may need to be addressed. For instance, these customers, administrators, and other users may access an analytics service and submit a request to obtain any temporal records having an end time that is greater than a specified start time. However, due to the nature of the storage of these temporal records over time, there may be overlapping temporal regions of data resulting from the back-filling of data, out of sync computer clocks, and the like. As a result of these overlaps, analytics services often provide a significant number of temporal records even if the overlap is minimal. This can prove to be inefficient and potentially very expensive as a result of the analysis needed for each temporal record to identify which data logs have the data they seek.